We use HRNet-w32 [1]}, [2]} pre-trained on the ImageNet dataset as our backbone network and Adam as our optimizer with a \(5\times 10^{-4}\)  learning rate and a \(10^{-6}\)  weight decay. All input images were padded to the same size of 512 × 832. Every prediction head attached to the backbone consists of one \(3 \times 3 \times 256\)  conv layer, BatchNorm, ReLU and another \(1 \times 1 \times c_0\)  conv layer, where \(c_0\)  is the output size. Following [3]}, we implemented multi-scale supervision from the four scale output of HRNet-w32 at the heatmap and part affinity field head. The model was trained for 50k iterations with a batch size of 32 on four RTX 3090 GPUs; \(50\%\)  of data in each mini-batch was from COCO dataset [4]}. The data augmentation process during the training included rotation, horizontal flips, and color jittering.
